Mapping super-resolution image quality

The local quality of super-resolution microscopy images can be assessed and mapped by rolling Fourier ring correlation, even when image quality varies within a single image.

instead.The Fourier-transformed spatial data from the image pairs are compared through a correlation function.The correlation will be high at frequencies (inverse length) containing meaningful information; at frequencies with unreliable information, the correlation will decay to zero.The frequency at which the correlation drops below a threshold yields the effective resolution of the image.While FRC is commonly used to assess image quality, it gives limited information about how resolution varies across an image, which can be caused by factors such as inhomogeneous labeling density and illumination, optical aberrations including defocusing, and detector defects.
In this issue of LSA, Zhao et al. introduce an approach to address this issue and map out local image quality at high spatial resolution 3 .Their rolling Fourier ring correlation (rFRC) method uses a scanning window to calculate the local FRC (Fig. 1a) and hence maps out the image quality pixel-by-pixel across the image (Fig. 1b).To account for systematic errors, the authors further incorporate an additional module to generate a resolutionscaled error map (RSM) 4 , which identifies artifacts of the reconstruction algorithm by comparing with a diffractionlimited reference image.The authors demonstrate the utility of the combined methodology on both simulated and experimental data.As the method is model-independent, the authors successfully assess super-resolution images acquired with a range of modalities, including 2D and 3D SMLM, SRRF, SIM, and others.
One enabling application of rFRC is the generation of super-resolution images using multiple reconstruction algorithms to achieve the highest resolution across the entire image, depicted in Fig. 2. The various algorithms available for SMLM reconstruction are often best suited for different conditions, such as low or high emitter density.Since emitter density is often heterogeneous across the sample, researchers may need to compromise on an algorithm that is suitable for all the local densities found in their sample.rFRC provides a path to locally determine the best reconstruction method and generate a fused image composed of the best parts of each reconstructed image.
The results reported by Zhao et al. reflect a contribution towards reproducible and quantitative super-resolution microscopy.With implementations for ImageJ, MATLAB, and Python, rFRC joins other open-source toolboxes such as NanoJ-SQUIRREL 4 , HAWKMAN 5 , and SIMCheck 6 for assessing super-resolution image quality, alongside the numerous available algorithms for reconstructing superresolution images from raw data.Ultimately, the resulting quality of super-resolution images is a product of both experimental parameters and the image processing steps used to create them.Quantitative analysis of image quality can generate more confidence in the results of super- resolution studies, avoid overinterpretation of images, and new paths towards even higher resolution.From a different perspective, by mapping out local image quality at the super-resolution level, rFRC also echoes recent efforts to extend super-resolution microscopy to new dimensions 7 .The possibility to encode new functional information 8 into such high-dimensional super-resolution maps, or, in the opposite direction, the generalization of rFRC to map out the local quality of or variations in other multidimensional super-resolution data, afford attractive prospects.

Fig. 1 RollingFig. 2
Fig. 1 Rolling Fourier ring correlation (rFRC) maps out the local quality in super-resolution images.a Starting with two images acquired under identical conditions, a window scans across the image pair so that the selected local super-resolution images are correlated through their Fourier transforms to obtain a local FRC resolution to be assigned to the center of the window.After sliding the window across the entire image, a map b of the FRC resolution is obtained, presenting the local image quality